It makes it possible to train any Weka classifier in Spark, for example. Conversely, Python toolkits such as scikit-learn can be used from Weka.įor running Weka-based algorithms on truly large datasets, the distributed Weka for Spark package is available. Weka's functionality can be accessed from Python using the Python Weka Wrapper. Weka models can be used, built, and evaluated in R by using the RWeka package for R conversely, R algorithms and visualization tools can be invoked from Weka using the RPlugin package for Weka. WEKA can be integrated with the most popular data science tools. Deep neural networks, including convolutional networks and recurrent networks, can be trained directly from Weka's graphical user interfaces, providing state-of-the-art methods for tasks such as image and text classification.
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WekaDeeplearning4j is a deep learning package for Weka. Weka Files Machine learning software to solve data mining problems Brought to you by: eibe, fracpete, mbatchelor, weka Summary Files Reviews Support News Download Latest Version weka-3-8-6-azul-zulu-windows.exe (133.2 MB) Home / weka-3-9 / 3.9. Note that programmers can also easily implement this pipeline using Weka's Java API: Second, we select a learning algorithm to use, e.g., the J48 classifier, which learns decision trees.įinally, we run a 10-fold cross-validation evaluation and obtain an estimate of predictive performance.
Weka can be used to build machine learning pipelines, train classifiers, and run evaluations without having to write a single line of code:įirst, we open the dataset that we would like to evaluate. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
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